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<p>The adoption of unmanned aerial vehicles (UAVs), particularly multirotor systems, is transforming precision agriculture (PA) by enabling versatile and cost-effective real-time, high-resolution data collection. This review aims to provide a comprehensive overview of the classification, applications, and advantages of multirotor UAVs in PA. A thorough literature survey and comparative analysis were conducted to examine recent technological innovations, challenges, and economic potential associated with these UAVs. The findings highlight that multirotor UAVs equipped with advanced sensors, artificial intelligence, and machine learning techniques improve crop monitoring, disease detection, and resource management. Despite challenges such as limited flight duration, payload constraints, regulatory issues, and data processing complexities, emerging advancements like improved battery technology, IoT integration, and UAV swarm operations show promise in overcoming these limitations. This study offers insights to guide future research and support sustainable agricultural practices, particularly in developing regions.</p>

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The expanding role of multirotor UAVs in precision agriculture with applications AI integration and future prospects

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月16日

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  • Sanket S. Unde,
  • V. K. Kurkute,
  • Sachin S. Chavan,
  • Dadaso D. Mohite,
  • Akshay A. Harale,
  • Ayaan Chougle

摘要

The adoption of unmanned aerial vehicles (UAVs), particularly multirotor systems, is transforming precision agriculture (PA) by enabling versatile and cost-effective real-time, high-resolution data collection. This review aims to provide a comprehensive overview of the classification, applications, and advantages of multirotor UAVs in PA. A thorough literature survey and comparative analysis were conducted to examine recent technological innovations, challenges, and economic potential associated with these UAVs. The findings highlight that multirotor UAVs equipped with advanced sensors, artificial intelligence, and machine learning techniques improve crop monitoring, disease detection, and resource management. Despite challenges such as limited flight duration, payload constraints, regulatory issues, and data processing complexities, emerging advancements like improved battery technology, IoT integration, and UAV swarm operations show promise in overcoming these limitations. This study offers insights to guide future research and support sustainable agricultural practices, particularly in developing regions.